1# Same data, different presentations for different audiences
2
3# For Executives
4"""
5Executive Summary:
6Revenue grew 15% ($2.3M) year-over-year, exceeding target by 3%.
7Key driver: New product line contributed 40% of growth.
8Recommendation: Increase marketing budget by $500K for Q2.
9"""
10
11# For Operations Team
12"""
13Q1 Performance:
14- Total revenue: $17.6M (+15% YoY)
15- Average order value: $127 (+8%)
16- Order volume: 138,500 (+6.5%)
17Action items:
181. Scale fulfillment capacity by 10%
192. Prioritize inventory for Product Line X
20"""
21
22# For Data Team
23"""
24Analysis Details:
25- Period: Q1 2024 vs Q1 2023, seasonally adjusted
26- Confidence: 95% CI [14.2%, 15.8%]
27- Caveats: Excludes one-time enterprise deal ($400K)
28"""

1# Act 1: Setup (Where are we?)
2"""
3Context: Our e-commerce platform serves 500K active customers.
4         Average order value is $85 with 2.3 orders per customer/year.
5"""
6
7# Act 2: Conflict (What's the problem?)
8"""
9Challenge: Q4 data shows concerning trends:
10- Cart abandonment increased from 68% to 76%
11- Mobile conversion dropped 15%
12Root cause: Checkout flow has 7 steps vs. industry standard of 3-4.
13"""
14
15# Act 3: Resolution (What should we do?)
16"""
17Solution: Redesign checkout to 4 steps
18Expected impact: Reduce abandonment by 10pp, recover $2.5M annually.
19Timeline: 6 weeks development, 2 weeks testing.
20"""

1import matplotlib.pyplot as plt
2import numpy as np
3
4categories = ['Product A', 'Product B', 'Product C', 'Product D', 'Product E']
5values = [23, 45, 12, 38, 29]
6
7fig, axes = plt.subplots(1, 2, figsize=(14, 5))
8
9# ❌ BAD: Pie chart for comparison
10axes[0].pie(values, labels=categories, autopct='%1.1f%%')
11axes[0].set_title('❌ BAD: Pie Chart for Comparison')
12
13# ✅ GOOD: Sorted bar chart
14sorted_idx = np.argsort(values)[::-1]
15sorted_cats = [categories[i] for i in sorted_idx]
16sorted_vals = [values[i] for i in sorted_idx]
17
18bars = axes[1].barh(sorted_cats, sorted_vals, color='steelblue')
19axes[1].set_xlabel('Sales ($K)')
20axes[1].set_title('✅ GOOD: Sorted Bar Chart')
21axes[1].bar_label(bars, fmt='$%dK')
22axes[1].invert_yaxis()
23plt.tight_layout()
24plt.show()

1fig, axes = plt.subplots(1, 2, figsize=(14, 5))
2
3months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
4sales = [120, 135, 128, 142, 138, 165]
5target = 150
6
7# ❌ BAD: No visual hierarchy
8axes[0].plot(months, sales, 'o-', linewidth=2)
9axes[0].axhline(target, linestyle='--', label='Target')
10axes[0].set_title('❌ BAD: No Visual Hierarchy')
11axes[0].legend()
12
13# ✅ GOOD: Highlight the insight
14colors = ['gray' if v < target else 'green' for v in sales]
15axes[1].bar(months, sales, color=colors)
16axes[1].axhline(target, color='red', linestyle='--', label='Target: $150K')
17axes[1].annotate('Hit target! ↑12%', xy=(5, 165), xytext=(4, 175),
18                 fontsize=11, fontweight='bold', color='green',
19                 arrowprops=dict(arrowstyle='->', color='green'))
20axes[1].set_ylabel('Sales ($K)')
21axes[1].set_title('✅ GOOD: Highlight Key Insight')
22axes[1].legend()
23plt.tight_layout()
24plt.show()

1# ❌ DON'T: Use jargon
2bad = "The YoY delta in the LTV:CAC ratio indicates deteriorating unit economics..."
3
4# ✅ DO: Use plain language
5good = """
6We're spending more to acquire customers who are worth less:
7- Cost to acquire a customer: up 23%
8- Revenue per customer: down 15%
9Bottom line: Each new customer takes 3 months longer to become profitable.
10"""

1import pandas as pd
2import numpy as np
3
4kpis = {
5    'Revenue': {'current': 2.5, 'target': 2.3, 'previous': 2.1, 'unit': 'M', 'higher_is_better': True},
6    'Customer Count': {'current': 12500, 'target': 13000, 'previous': 11800, 'unit': '', 'higher_is_better': True},
7    'Churn Rate': {'current': 5.2, 'target': 4.0, 'previous': 4.8, 'unit': '%', 'higher_is_better': False},
8    'NPS': {'current': 42, 'target': 50, 'previous': 38, 'unit': '', 'higher_is_better': True},
9    'CAC': {'current': 125, 'target': 100, 'previous': 145, 'unit': '$', 'higher_is_better': False}
10}
11
12def calculate_kpi_status(kpi_data):
13    results = []
14    for name, data in kpi_data.items():
15        current, target, previous = data['current'], data['target'], data['previous']
16        higher_better = data['higher_is_better']
17        
18        vs_target = ((current - target) / target) * 100
19        vs_previous = ((current - previous) / previous) * 100
20        
21        on_track = current >= target if higher_better else current <= target
22        status = '✅' if on_track else '⚠️' if abs(vs_target) < 10 else '❌'
23        
24        results.append({
25            'KPI': name, 'Current': f"{current}{data['unit']}",
26            'Target': f"{target}{data['unit']}",
27            'vs Target': f"{'+' if vs_target > 0 else ''}{vs_target:.1f}%",
28            'vs Previous': f"{'+' if vs_previous > 0 else ''}{vs_previous:.1f}%",
29            'Status': status
30        })
31    return pd.DataFrame(results)
32
33print("Executive KPI Summary")
34print("=" * 70)
35print(calculate_kpi_status(kpis).to_string(index=False))

1def create_status_indicator(value, target, higher_is_better=True):
2    if higher_is_better:
3        pct = (value - target) / target * 100
4    else:
5        pct = (target - value) / target * 100
6    
7    if pct >= 0:
8        return '🟢', 'On Track'
9    elif pct >= -10:
10        return '🟡', 'Monitor'
11    else:
12        return '🔴', 'At Risk'
13
14metrics = [
15    ('Revenue', 2.5, 2.3, True),
16    ('Customers', 12500, 13000, True),
17    ('Churn Rate', 5.2, 4.0, False),
18    ('CAC', 125, 100, False),
19    ('NPS', 42, 50, True)
20]
21
22print("KPI Status Summary")
23print("=" * 50)
24for name, actual, target, higher_better in metrics:
25    light, status = create_status_indicator(actual, target, higher_better)
26    print(f"{light} {name:15s} | Actual: {actual:>8} | Target: {target:>8} | {status}")

1import matplotlib.pyplot as plt
2import numpy as np
3
4fig = plt.figure(figsize=(16, 10))
5fig.suptitle('Q1 2024 Executive Dashboard', fontsize=16, fontweight='bold', y=0.98)
6gs = fig.add_gridspec(3, 4, hspace=0.3, wspace=0.3)
7
8# Row 1: Big Numbers
9for i, (label, value, change, color) in enumerate([
10    ('Revenue', '$8.2M', '+15% vs LY', 'green'),
11    ('Customers', '15,200', '+22% vs LY', 'steelblue'),
12    ('NPS', '48', '-2 vs target', 'orange'),
13    ('Margin', '68%', '+3pp vs LY', 'green')
14]):
15    ax = fig.add_subplot(gs[0, i])
16    ax.text(0.5, 0.7, value, fontsize=28, fontweight='bold', ha='center', va='center', color=color)
17    ax.text(0.5, 0.3, f'{label}\n{change}', fontsize=10, ha='center', va='center', color='gray')
18    ax.axis('off')
19
20# Row 2: Trend
21ax5 = fig.add_subplot(gs[1, :2])
22months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
23actual = [2.1, 2.3, 2.5, 2.6, 2.8, 3.0]
24target = [2.0, 2.2, 2.4, 2.5, 2.7, 2.9]
25ax5.fill_between(months, actual, alpha=0.3, color='green')
26ax5.plot(months, actual, 'go-', linewidth=2, label='Actual')
27ax5.plot(months, target, 'k--', label='Target')
28ax5.set_ylabel('Revenue ($M)')
29ax5.set_title('Revenue Trend', fontweight='bold')
30ax5.legend(loc='upper left')
31
32# Row 2: Segment breakdown
33ax6 = fig.add_subplot(gs[1, 2:])
34segments = ['Enterprise', 'Mid-Market', 'SMB', 'Consumer']
35values = [3.5, 2.8, 1.5, 0.4]
36colors = ['#2ecc71', '#3498db', '#9b59b6', '#e74c3c']
37bars = ax6.barh(segments, values, color=colors)
38ax6.set_xlabel('Revenue ($M)')
39ax6.set_title('Revenue by Segment', fontweight='bold')
40ax6.bar_label(bars, fmt='$%.1fM')
41
42# Row 3: Issues & Actions
43for i, (title, items) in enumerate([
44    ('🔴 KEY ISSUES', ['CAC increased 18%', 'Support backlog at 72hrs', 'Enterprise deal slipped to Q2']),
45    ('🟢 KEY ACTIONS', ['Launch loyalty program → +5% retention', 'Deploy mobile app v2 → +10% engagement', 'Close 3 enterprise deals → +$800K'])
46]):
47    ax = fig.add_subplot(gs[2, i*2:(i+1)*2])
48    ax.axis('off')
49    text = f"{title}\n" + "\n".join([f"  {j+1}. {item}" for j, item in enumerate(items)])
50    ax.text(0.05, 0.95, text, fontsize=10, fontfamily='monospace',
51            verticalalignment='top', transform=ax.transAxes)
52
53plt.savefig('executive_dashboard.png', dpi=150, bbox_inches='tight', facecolor='white')
54plt.show()

1from jinja2 import Template
2from datetime import datetime
3
4report_template = Template("""
5━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
6                    {{ title }}
7                    {{ date }}
8━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
9
10�� KEY METRICS
11{% for metric in metrics %}
12{{ metric.status }} {{ metric.name }}: {{ metric.value }} (Target: {{ metric.target }})
13{% endfor %}
14
15�� HIGHLIGHTS
16{% for highlight in highlights %}
17• {{ highlight }}
18{% endfor %}
19
20�� ACTION ITEMS
21{% for action in actions %}
22{{ loop.index }}. {{ action.task }} (Owner: {{ action.owner }}, Due: {{ action.due }})
23{% endfor %}
24━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
25""")
26
27data = {
28    'title': 'WEEKLY SALES REPORT',
29    'date': datetime.now().strftime('%Y-%m-%d'),
30    'metrics': [
31        {'name': 'Revenue', 'value': '$2.5M', 'target': '$2.3M', 'status': '✅'},
32        {'name': 'Orders', 'value': '12,500', 'target': '11,000', 'status': '✅'},
33        {'name': 'Conversion', 'value': '3.2%', 'target': '3.5%', 'status': '⚠️'},
34    ],
35    'highlights': [
36        'Record Thursday revenue (+45% vs average)',
37        'Mobile app orders grew 25% MoM'
38    ],
39    'actions': [
40        {'task': 'Review mobile checkout UX', 'owner': 'Product', 'due': '3/22'},
41        {'task': 'Launch retargeting campaign', 'owner': 'Marketing', 'due': '3/20'},
42    ]
43}
44
45print(report_template.render(**data))

1import logging
2
3logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
4logger = logging.getLogger(__name__)
5
6class AutomatedReportingSystem:
7    def __init__(self, config):
8        self.config = config
9        self.data = {}
10        self.metrics = {}
11    
12    def extract_data(self):
13        logger.info("Extracting data...")
14        # In production: query database, APIs
15        import numpy as np
16        self.data['revenue'] = pd.DataFrame({
17            'date': pd.date_range(start='2024-01-01', periods=90, freq='D'),
18            'revenue': np.random.uniform(70000, 100000, 90),
19            'orders': np.random.randint(300, 500, 90)
20        })
21        logger.info("Data extraction complete")
22    
23    def calculate_metrics(self):
24        logger.info("Calculating metrics...")
25        rev_df = self.data['revenue']
26        self.metrics = {
27            'total_revenue': rev_df['revenue'].sum(),
28            'avg_daily_revenue': rev_df['revenue'].mean(),
29            'total_orders': rev_df['orders'].sum(),
30            'avg_order_value': rev_df['revenue'].sum() / rev_df['orders'].sum()
31        }
32        logger.info("Metrics calculation complete")
33    
34    def generate_report(self):
35        logger.info("Generating report...")
36        m = self.metrics
37        return f"""
38Total Revenue: ${m['total_revenue']:,.0f}
39Average Daily: ${m['avg_daily_revenue']:,.0f}
40Total Orders: {m['total_orders']:,}
41AOV: ${m['avg_order_value']:.2f}
42"""
43    
44    def run_pipeline(self):
45        logger.info("Starting pipeline...")
46        self.extract_data()
47        self.calculate_metrics()
48        report = self.generate_report()
49        logger.info("Pipeline completed!")
50        return report
51
52# Run
53system = AutomatedReportingSystem({'db': 'example'})
54print(system.run_pipeline())

1# Schedule configuration
2schedule_config = {
3    'daily_flash': {'time': '07:00', 'recipients': ['ceo@company.com']},
4    'weekly_summary': {'day': 'Monday', 'time': '08:00', 'recipients': ['leadership@company.com']},
5    'monthly_review': {'day': 1, 'time': '09:00', 'recipients': ['board@company.com']}
6}
7
8# APScheduler example
9"""
10from apscheduler.schedulers.background import BackgroundScheduler
11from apscheduler.triggers.cron import CronTrigger
12
13scheduler = BackgroundScheduler()
14scheduler.add_job(daily_report, CronTrigger(hour=7, minute=0), id='daily')
15scheduler.add_job(weekly_report, CronTrigger(day_of_week='mon', hour=8), id='weekly')
16scheduler.start()
17"""